OpenAI describes a method for simulating model deployment before release

OpenAI says it has developed a method called Deployment Simulation to estimate how a candidate model may behave before it is released. The approach replays recent productionlike conversations with a new model to study whether undesirable behaviors appear in realistic contexts and how often they might occur. According to the post, the method was tested across GPT5series Thinking deployments and on internal agentic coding rollouts. OpenAI says the simulations helped improve estimates of misalignment and disallowed content, revealed a new issue called calculator hacking before release, and reduced the chance that models would recognize they were being evaluated. The company also says the technique can be used with toolheavy agent settings by simulating tool calls rather than connecting to live systems. OpenAI describes the method as a complement to redteaming and targeted safety evaluations, not a replacement, and says it is intended to make predeployment risk assessment more realistic and measurable.